Exponential periodicity and stability of delayed neural networks
Mathematics and Computers in Simulation
Global robust stability of interval neural networks with multiple time-varying delays
Mathematics and Computers in Simulation
Mathematics and Computers in Simulation
A new criterion for global robust stability of interval delayed neural networks
Journal of Computational and Applied Mathematics
Expert Systems with Applications: An International Journal
Stationary oscillation for cellular neural networks with time delays and impulses
Mathematics and Computers in Simulation
Stability analysis of discrete-time recurrent neural networks with stochastic delay
IEEE Transactions on Neural Networks
A new method for stability analysis of recurrent neural networks with interval time-varying delay
IEEE Transactions on Neural Networks
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
IEEE Transactions on Neural Networks
Exponential stability on stochastic neural networks with discrete interval and distributed delays
IEEE Transactions on Neural Networks
Existence, learning, and replication of periodic motions in recurrent neural networks
IEEE Transactions on Neural Networks
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In this paper, a class of impulsive interval neural networks with discrete and distributed time-varying delays is discussed. Several new sufficient conditions are obtained ensuring the existence, uniqueness, and global exponential stability of periodic solution (i.e., stationary oscillation) for the addressed models based on inequality analysis techniques. The obtained results can be checked easily by the linear matrix inequality control toolbox in MATLAB. Finally, two numerical examples are given to show the effectiveness of our results.